GITNUXSOFTWARE ADVICE
General KnowledgeTop 10 Best Reviewing Software of 2026
Top 10 Reviewing Software ranking for teams comparing InVision, Figma, and Zeplin across review workflows, features, and tradeoffs.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
InVision
Comment threads anchored to prototype frames enable targeted feedback workflows.
Built for fits when design teams need automated prototype publishing and governed review access..
Figma
Editor pickVariables plus components create a structured design token schema consumable by plugins and API scripts.
Built for fits when product teams need governed automation around a shared design data model..
Zeplin
Editor pickComponent and style token extraction into a reusable, schema-like handoff model.
Built for fits when product teams need controlled design handoff with API-visible metadata..
Related reading
Comparison Table
This comparison table reviews reviewing software across integration depth, focusing on how design, project tracking, and workflow tools exchange assets via API and automation. It also maps each product’s data model and schema, including extensibility options and provisioning paths, then compares admin and governance controls such as RBAC, audit logs, and configuration controls. Automation and API surface, plus how teams manage throughput in real workflows, provide the basis for the tradeoff notes.
InVision
design reviewBrowser-based design review with per-file comments, versioning, and review links for stakeholder feedback workflows.
Comment threads anchored to prototype frames enable targeted feedback workflows.
InVision supports prototype creation with interaction hotspots, transitions, and responsive previews tied to design source updates. Review features capture comments per frame or area and organize threads inside named projects. Integration depth matters for enterprises that need InVision content to appear inside internal tools, with webhooks and API endpoints used for automation and content synchronization. A concrete governance requirement is how RBAC limits who can create, publish, and manage review spaces.
A tradeoff appears in schema rigidity for automation because InVision centers activity around projects, prototypes, and review threads rather than a fully custom data model. High-throughput feedback sessions can generate large comment and version histories that require careful retention and permission planning. InVision works well when design teams must publish prototypes for cross-team review and keep feedback attached to specific prototype states.
- +Prototype-to-feedback review links with frame-specific commenting
- +API and automation surface for syncing prototype assets
- +RBAC controls for publishing and review space access
- +Integration options for connecting review workflows
- –Automation data model centers on projects and reviews
- –High comment volume can increase governance and cleanup effort
- –Extensibility depends on supported endpoints and webhooks
Design operations teams
Standardize prototype reviews across products
Fewer mismatched feedback cycles
Platform engineering teams
Automate prototype publishing into internal tools
Lower manual publishing effort
Show 2 more scenarios
Product managers
Coordinate stakeholder feedback on prototypes
Clearer decision trails
Frame-level comments consolidate approvals and revisions inside structured review spaces.
IT governance leads
Control access to design review assets
Reduced access risk
RBAC permissions restrict who can publish, comment, and administer review spaces.
Best for: Fits when design teams need automated prototype publishing and governed review access.
Figma
collaborative reviewCollaboration and review using share links, threaded comments on frames, and role-based access controls with audit-relevant activity history.
Variables plus components create a structured design token schema consumable by plugins and API scripts.
Figma fits teams that need shared design context across disciplines, with work happening in real time across a single file. The component and variable model reduces drift by standardizing reusable UI parts and design tokens. Version history and review links support traceable changes without duplicating artifacts across tools.
Automation and extensibility are strongest when plugins and API-driven workflows can act on the node graph in a file, such as extracting design tokens or generating documentation from structured layers. A tradeoff appears for governance-heavy orgs, because cross-team control often requires disciplined role assignment and consistent naming and file conventions. Best fit shows up in organizations that can treat Figma as an authoritative design schema and automate downstream handoffs into dev workflows.
- +Node-level API enables automation on frames, layers, and components
- +Plugin ecosystem provides extensibility without rebuilding design tooling
- +Variables and components encode reusable UI structure
- +Comments, mentions, and version history support structured review
- –Admin and RBAC controls need process to prevent permission sprawl
- –Automation depends on plugin runtime limits and file access boundaries
- –Large design systems can stress review workflows and file organization
Design ops teams
Standardize tokens across multi-team designs
Fewer inconsistencies in UI theming
Frontend platform teams
Generate UI assets from design nodes
Reduced manual handoff work
Show 2 more scenarios
Product managers
Run review workflows on prototypes
Clear decision history
Comment threads tie feedback to specific frames and versions for audits.
Security and IT governance
Control access across many files
Lower risk of unintended exposure
RBAC and permission scoping require consistent provisioning practices and review of access paths.
Best for: Fits when product teams need governed automation around a shared design data model.
Zeplin
handoff reviewDesign handoff with exportable specs and in-browser comments tied to designs for structured review cycles between designers and engineers.
Component and style token extraction into a reusable, schema-like handoff model.
Zeplin supports design handoff with a UI that maps design exports into a repeatable structure for engineering review. The data model includes screens, components, tokens, and style properties so teams can reuse definitions instead of translating from images. Integration depth is strongest inside the design-to-spec workflow because Zeplin focuses on provisioning structured artifacts rather than general file sync. Automation and API surface are oriented around handoff access, metadata retrieval, and workflow events tied to projects and assets.
A tradeoff is that Zeplin automation is not a full orchestration layer for CI or runtime configuration, because it primarily manages handoff artifacts and their metadata. Teams see best results when product designers and front-end developers need a single source of truth for spacing, typography, and component states across multiple iterations. Usage is most effective when governance rules must be applied consistently across projects with clear access control and auditability.
- +Structured design-to-spec data model for screens, components, and style tokens
- +Project-level access controls with RBAC for multi-team governance
- +Automation and API surface oriented around handoff assets and metadata
- –Limited CI-level orchestration compared with deeper workflow automation tools
- –API coverage is narrower than tools that manage end-to-end delivery pipelines
Front-end engineering teams
Convert Figma specs into consistent UI details
Fewer spec translation errors
Design systems teams
Centralize components and style tokens for reuse
More consistent UI implementation
Show 2 more scenarios
Engineering operations
Govern handoff access across projects
Controlled collaboration boundaries
Admins apply RBAC and manage project visibility for designers and engineers at scale.
Product delivery teams
Track iterative updates for released features
Tighter release alignment
Teams coordinate handoff updates so changes propagate through the same artifact model.
Best for: Fits when product teams need controlled design handoff with API-visible metadata.
Miro
visual collaborationVisual collaboration with real-time co-editing and comment threads tied to specific objects on boards for review workflows.
Miro REST API plus webhooks for board events and automated element workflows.
Miro supports collaborative visual planning with boards, embedded assets, and real-time editing. Its integration depth is driven by a documented API, webhooks, and Marketplace apps that connect external tools to specific board objects.
Miro’s data model organizes content into frames, shapes, comments, and workspaces, which makes RBAC and content governance more enforceable. Automation and extensibility rely on API operations and app configuration that target identity, permissions, and workspace boundaries.
- +REST API covers boards, users, and elements for automation
- +Webhooks enable event-driven sync for board and asset changes
- +Extensibility supports Marketplace integrations and custom apps
- +RBAC plus workspace roles reduce cross-team data exposure
- +Audit-oriented admin settings support operational governance
- –Granular schema access for every element type is inconsistent
- –Automation throughput can be limited by rate constraints
- –Admin governance lacks fine-grained per-object permission controls
- –Complex board state changes can require multi-step reconciliation
- –Sandbox and test harness support for apps is limited
Best for: Fits when distributed teams need board-level automation and controlled integrations across workspaces.
Perforce Helix ALM
requirements governanceRequirements and change management with review states, approval workflows, and traceability across work items for governed reviews.
Workflow configuration tied to a schema-driven work item model with audit-tracked governance changes.
Perforce Helix ALM provisions requirements, test management, and defect workflows with a configurable data model and governance controls. Integration depth comes from connecting ALM artifacts to Perforce version control and automating status updates across lifecycle events.
Automation and extensibility are driven through an API surface and event-driven hooks for workflow execution and reporting. Administration centers on role-based access, schema governance, and auditability for changes to projects and configuration.
- +Schema-driven data model for work items across requirements, tests, and defects
- +Tight integration with Perforce workflows and changelists for traceability
- +API-driven automation supports provisioning, updates, and custom reporting
- +RBAC controls limit access by project scope and workflow permissions
- +Audit logs capture configuration and governance changes
- –Admin configuration can be heavy for organizations needing minimal workflow customization
- –Automation surface requires careful event and state modeling to avoid workflow loops
- –Cross-system integrations depend on custom API and connector work for non-Perforce tools
- –Throughput can degrade with high-frequency automation and large work item volumes
- –Versioning of workflow and schema changes adds operational overhead
Best for: Fits when teams need Perforce-linked ALM automation with governed schemas and RBAC.
Atlassian Jira
workflow reviewsWorkflow-driven issue reviews using configurable transitions, approvals via marketplace apps, and audit logs for governance controls.
Jira Automation rules with event triggers, conditions, and scheduled actions
Atlassian Jira fits teams that need a strict work tracking data model with extensible workflows and automation. Jira’s schema-centric issue model supports configurable fields, issue types, screens, and workflow transitions.
Integration depth comes from Jira’s REST APIs, automation rules, and add-ons built against Jira’s extensibility points. Admin control is driven by project roles, permission schemes, audit logging, and governance around shared configurations and workflow changes.
- +Configurable issue data model with fields, screens, and workflow transitions
- +REST API coverage for issue, project, user, and workflow operations
- +Automation rules support event triggers, conditions, and scheduled executions
- +Permission schemes and project roles provide RBAC-style access control
- +Audit log records configuration and permission-impacting changes
- –Workflow and permission configuration can become complex at scale
- –API-driven customizations require careful schema management
- –Automation rule logic can be hard to debug across many projects
- –Cross-project reporting depends on consistent taxonomy and field usage
Best for: Fits when teams need governed issue schemas with API and automation-driven workflow control.
Atlassian Confluence
document reviewDocument collaboration with page-level comments, version history, and granular permissions for review and sign-off processes.
Confluence REST API with webhooks for page and attachment lifecycle automation.
Atlassian Confluence ties documentation, knowledge bases, and team spaces into a unified permissioned content model built around pages and attachments. Its integration depth with Jira and Atlassian identity centers on project and group context, plus workflow-aware linking.
Automation and extensibility are driven by REST APIs, webhooks, and Connect or Forge apps, enabling schema-adjacent operations like content creation, permission updates, and search indexing. Admin governance uses Atlassian-managed controls such as RBAC, space/page restrictions, and audit logs for access and configuration changes.
- +Tight Jira integration via smart links and issue-context macros
- +Clear data model using spaces, pages, attachments, and labels
- +REST API and webhooks cover content lifecycle and metadata operations
- +RBAC supports space-level permissions and granular group access
- +Audit log records admin and security-relevant changes
- –Custom schema is limited since core model is page-centric
- –Permission inheritance can confuse audits for deeply nested spaces
- –Automation patterns often require app scaffolding for complex workflows
- –Bulk operations can hit throughput limits during large migrations
- –Search relevance tuning is constrained for highly specialized taxonomies
Best for: Fits when teams need Jira-linked documentation with API-driven automation and governed access.
GitHub Pull Requests
code reviewCode review through pull requests with review comments, required status checks, branch protection, and audit logs.
Branch protection with required reviews and status checks enforces policy at merge time.
GitHub Pull Requests adds review workflow data directly to the GitHub pull request object, tying code diffs, discussion, checks, and approvals to one record. Integration depth comes from tight connections to GitHub Actions, status checks, and branch protection settings that gate merges.
The data model supports review states, requested reviewers, code owners, and commit status contexts that can be queried through the GitHub API. Automation and governance are driven through webhooks, REST and GraphQL endpoints, and RBAC roles for teams and organizations.
- +PR object links diffs, comments, reviews, and mergeability in one data model
- +Branch protection enforces required reviews and status checks before merge
- +GitHub Actions and checks integrate with merge gates using status contexts
- +REST and GraphQL APIs expose review, approval, and merge state for automation
- +Webhooks send PR, review, and check events for external systems
- –Automation requires API wiring for custom governance rules beyond branch protection
- –Review policy changes can create merge friction across protected branches
- –High-volume review threads can strain search and retrieval performance
- –Granular audit needs rely on organization settings and correct RBAC configuration
Best for: Fits when GitHub-centered teams need merge governance, review metadata, and API-driven automation.
GitLab Merge Requests
code reviewMerge-request review with threaded comments, approvals, merge checks, and audit logging tied to project governance settings.
Merge request approvals with rule-based eligibility via branch protections and authorization scopes.
GitLab Merge Requests coordinates code changes through a merge request data model that links diffs, approvals, checks, and pipelines. GitLab integrates review workflow with branch protection, RBAC, required approvals, and protected environments so governance gates run before merge.
Automation and integration rely on a documented API that can create merge requests, manage discussions and approvals, and trigger pipeline runs. Admin controls include audit logging and policy configuration that bind merge eligibility to organization settings and project rules.
- +Merge request schema ties diffs, approvals, and pipelines into one workflow object
- +Branch protection and required approvals enforce governance at merge time
- +API supports creating requests, managing notes, and updating approval state
- +CI integration gates merges using pipeline status checks
- –Multi-step review state increases configuration complexity across projects
- –Automation often requires careful event wiring to keep approvals consistent
- –Large repositories can create throughput limits for diff and pipeline checks
Best for: Fits when teams need merge governance enforced by RBAC and pipeline checks via API automation.
Bitbucket Pull Requests
code reviewPull request review with in-line comments, approval requirements, and repository permissions with audit-log visibility.
Merge checks and branch permissions enforce governance using PR-aware merge rules.
Bitbucket Pull Requests fits teams that want PR-centric workflows tightly coupled to Bitbucket repositories and branch history. It centers on a data model of PRs, commits, diffs, approvals, and reviewers that drives gating via branch permissions and merge checks.
Integration depth is driven by Jira issue linking, webhook events, and branch- or repository-scoped settings that control who can create, comment, approve, and merge. Automation is mainly expressed through webhooks, REST APIs, and merge-guard configuration, with audit trails recorded for review actions and rule enforcement.
- +PR data model links reviewers, approvals, and diffs to merge behavior
- +Webhook events and REST API cover PR lifecycle and review events
- +Jira issue linking keeps PR metadata synchronized with development work
- +Merge checks and branch permissions enforce governance at merge time
- –Fine-grained automation often requires stitching multiple APIs and webhooks
- –Approval and review rule complexity can require careful configuration
- –Cross-repo governance depends on external policy logic and integrations
- –Extensibility for custom workflows is narrower than dedicated workflow engines
Best for: Fits when mid-size teams need PR workflow control tied to Bitbucket permissions and audit trails.
How to Choose the Right Reviewing Software
This buyer's guide covers reviewing software workflows across InVision, Figma, Zeplin, Miro, Perforce Helix ALM, Atlassian Jira, Atlassian Confluence, GitHub Pull Requests, GitLab Merge Requests, and Bitbucket Pull Requests.
The guidance focuses on integration depth, the underlying data model, automation and API surface, and admin and governance controls. It maps those mechanics to real review workflows like prototype feedback, tokenized design handoff, board object review, and merge-time approval gates.
Review workflow platforms that attach feedback to objects, states, and merge gates
Reviewing software captures feedback and approvals tied to review targets like prototypes, design frames, handoff assets, boards, work items, pull requests, and merge requests. The core job is linking comments and decisions to a structured object model so teams can route changes, track status, and enforce policy.
InVision and Figma anchor review comments to specific design objects like prototype frames and nodes inside a file data model. Atlassian Jira and Perforce Helix ALM attach approvals and review states to schema-driven work items with governance controls that regulate who can progress what.
Integration, data modeling, automation surface, and governance controls that make reviews enforceable
Review tools differ most in how they model review targets and how they expose that model through APIs, webhooks, and automation rules. Integration depth matters because review artifacts often must sync to design systems, planning boards, work items, and CI checks.
Admin governance matters because review activity and access control must stay consistent across spaces, projects, repositories, and work item schemas. These evaluation criteria focus on the mechanisms that control configuration sprawl and audit traceability, not on UI alone.
Object-anchored comment threads tied to a review target model
InVision anchors comment threads to prototype frames so feedback lands on the exact element being reviewed. Figma uses node-level structure so comments map to frames, layers, and components inside the file data model.
Extensible design schema via variables, components, and token extraction
Figma pairs variables and components to form a reusable design token schema that plugins and API scripts can consume. Zeplin extracts component and style token metadata into a schema-like handoff model for structured engineering review.
API and webhook coverage that matches automation goals
Miro combines a REST API for boards and elements with webhooks that stream board events for event-driven synchronization. GitHub Pull Requests and GitLab Merge Requests expose merge, review, and check states through API and webhook events that automation can gate on.
Workflow state and approval rules bound to a governed data model
Perforce Helix ALM uses a schema-driven work item model with workflow configuration tied to requirements, tests, and defects plus audit-tracked governance changes. Atlassian Jira uses configurable issue schemas and workflow transitions plus Automation rules with event triggers, conditions, and scheduled actions.
RBAC and admin governance that limit access and preserve audit trails
Figma uses role-based access controls that align with audit-relevant activity history, which helps keep review access consistent at scale. Jira and Confluence record admin and security-relevant changes in audit logs while using project roles and space or page restrictions to control review permissions.
Merge-time enforcement through branch protections and merge checks
GitHub Pull Requests gates merges through branch protection with required reviews and required status checks. GitLab Merge Requests and Bitbucket Pull Requests enforce governance using merge checks tied to authorization scopes and repository or branch permissions.
A decision path from review artifact model to automation and governance enforcement
Start by mapping the review object that needs feedback and approval. Then confirm the data model that the tool uses to anchor comments and state changes so automation can reliably query and update the right entities.
Next, validate that the API and webhook surface covers the automation events required for the workflow. Finally, test governance depth by checking how RBAC, audit logs, and permission boundaries behave in multi-team or multi-project environments.
Pick the review target model that matches the object stakeholders review
If stakeholders review prototypes with frame-level feedback, InVision is a direct match because comment threads attach to prototype frames tied to review links. If stakeholders review a shared design data model with nodes, frames, layers, and reusable tokens, Figma fits because its API works at node level and variables support a token schema.
Choose the schema and metadata path for downstream automation
If engineering needs a schema-like handoff built from component and style token extraction, Zeplin provides that reusable metadata model for structured review cycles. If the review needs board object automation across workspaces, Miro’s content model organizes boards into frames, shapes, and comment threads that its REST API can target.
Map automation events to the tool’s API and webhook surface
For event-driven syncing of board and element changes, Miro’s REST API plus webhooks support automated element workflows. For merge gating and automated checks, GitHub Pull Requests enforces policy using branch protection with required reviews and status checks, and GitLab Merge Requests uses merge eligibility tied to required approvals and pipeline checks through API-accessible workflow objects.
Require governed workflow state tied to RBAC and audit logs
If review decisions must move through configurable workflow states on schema-driven work items, Perforce Helix ALM links workflow configuration to a schema model plus audit-tracked governance changes. If review decisions must follow Jira issue schemas with automation-driven transitions, Atlassian Jira provides REST API coverage and Automation rules with event triggers, conditions, and scheduled actions.
Validate admin permission boundaries for scale
If permission sprawl risk exists across teams, focus on how Figma manages RBAC and audit-relevant activity history and how its admin process prevents permission sprawl. If review content spans documentation hierarchies, Confluence uses RBAC with space and page restrictions plus audit logs, and nested space permission inheritance can complicate audit interpretation.
Which teams should match review mechanics to automation depth and governance scope
Reviewing software is a fit when feedback and approvals must be attached to structured review objects and tracked through state transitions or merge gates. It is also a fit when automation must sync review outcomes into adjacent workflow systems like CI checks, issue tracking, handoff metadata, or board artifacts.
The best matches depend on whether the primary review object is design, board content, work item state, or pull request and merge request governance.
Design teams running prototype feedback workflows
InVision fits because it anchors comment threads to prototype frames and publishes review links for stakeholder feedback workflows. The API and automation surface supports syncing prototype assets while RBAC controls determine who can publish and comment within review spaces.
Product teams needing governed automation against a shared design model
Figma fits because its data model centers on files, pages, frames, nodes, and design metadata, and its node-level API supports automation on frames, layers, and components. Its variables and components create a structured token schema that plugins and API scripts can consume for review automation.
Product and engineering teams requiring controlled design handoff metadata
Zeplin fits because it extracts component and style token metadata into a schema-like handoff model with exportable specs and in-browser comments tied to designs. Its project-level access controls support RBAC governance for multi-team work.
Distributed teams coordinating review across boards and workspace boundaries
Miro fits because its REST API covers boards, users, and elements and webhooks stream board events for automated element workflows. Its RBAC plus workspace roles reduce cross-team data exposure, even when board state changes require careful reconciliation.
Engineering teams enforcing merge-time approval policies via repository governance
GitHub Pull Requests fits when merge gates depend on branch protection with required reviews and required status checks. GitLab Merge Requests and Bitbucket Pull Requests fit when merge eligibility must follow RBAC plus pipeline checks through API automation and merge checks tied to branch protections and repository permissions.
Pitfalls that break review governance, automation reliability, and admin control at scale
Common failure modes come from choosing a tool whose automation and data model do not match how reviews must be anchored and enforced. Other failures come from underestimating governance configuration complexity and from wiring automation loops across state transitions.
These pitfalls show up as missing automation coverage, hard-to-debug workflow logic, or permission structures that confuse audit traceability.
Treating comment threads as interchangeable with workflow approvals
Use InVision or Figma for frame-anchored feedback, but add Jira, Perforce Helix ALM, or merge-request tooling when approvals must drive state transitions or merge gates. Jira Automation rules and Perforce Helix ALM workflow configuration bind review progress to governed work item states rather than just leaving comments.
Assuming the API surface supports end-to-end orchestration
If automation must orchestrate CI gates and merge eligibility, prefer GitHub Pull Requests with branch protection and required status checks or GitLab Merge Requests with pipeline checks. If automation needs broad board object change throughput, Miro can stream events through webhooks but rate constraints can limit high-throughput sync.
Building permission processes that create audit confusion
Plan RBAC and permission inheritance carefully in Confluence because permission inheritance can confuse audits for deeply nested spaces. For Figma, define a process that prevents permission sprawl since admin and RBAC controls require operational discipline at scale.
Over-configuring workflow transitions and automation logic without governance discipline
Jira can become complex when workflow and permission configuration grows across projects, and Automation rule logic can be hard to debug across many projects. Perforce Helix ALM automation also requires careful event and state modeling to avoid workflow loops.
How We Selected and Ranked These Tools
We evaluated InVision, Figma, Zeplin, Miro, Perforce Helix ALM, Atlassian Jira, Atlassian Confluence, GitHub Pull Requests, GitLab Merge Requests, and Bitbucket Pull Requests using criteria centered on features, ease of use, and value, then calculated an overall rating as a weighted average where features carry the most weight and ease of use and value share the remaining weight equally. Features received the highest emphasis because review governance depends on anchored objects, schema structure, and automation hooks like REST APIs, webhooks, and workflow transitions.
InVision separated itself from lower-ranked options through its comment threads anchored to prototype frames paired with an API and automation surface that syncs prototype assets. That combination lifted the overall score by strengthening the integration and automation path for stakeholder feedback workflows while keeping review access governed via RBAC controls for publishing and review space access.
Frequently Asked Questions About Reviewing Software
Which reviewing tool best matches design feedback workflows tied to interactive prototypes?
How do Figma and Zeplin differ for governed handoff artifacts between design and engineering?
What integration and API capabilities matter most when automating review pipelines?
Which tools provide the strongest merge governance gates via repository permissions?
How do admin controls and RBAC differ across Jira, Confluence, and version-control PR systems?
Which option fits multi-team governance when review content must live inside a structured data model?
What is the typical data migration approach when moving review work from one system to another?
How do Confluence and Jira integration patterns affect review traceability?
What common problems appear during implementation and how do tools reduce them?
Which tool is most suitable for review extensibility when integrations must target specific objects and events?
Conclusion
After evaluating 10 general knowledge, InVision stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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